While the presence and rate of glaucoma progression influences treatment decisions, the methods currently available to detect and monitor progression are imprecise and do not allow clinicians to make accurate assessments of the status of their patients. The long-term goal of this project is to reduce the time needed to detect glaucoma progression. We developed an individualized model to detect progression that uses structural and functional data jointly. The specific aims are 1) To determine whether an individualized approach to identify glaucoma progression leads to earlier detection of progression compared to current methods based on population statistics, 2) To determine which combination of structural and functional parameters identifies glaucoma progression at the earliest point in time, and 3) To determine the shortest period of time needed for our individualized approach to detect glaucoma progression. Specific aims 1 and 2 will use the data prospectively collected in two large multi-center NIH-funded studies: the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study. These studies are ideally suited to achieve these aims because of the large sample of longitudinal structural and functional data. For specific aim 3, we will prospectively collect data from glaucoma patients seen at our institution. Our central hypothesis is that combining structural and functional data within the framework of an individualized model will improve our ability to detect glaucoma progression more rapidly. The dynamic structure-function model is innovative in that it is individualized and robust to assumptions about the nature of glaucoma progression. We hypothesize that our individualized dynamic structure-function model will lead to the detection of progression at an earlier point in time compared to other currently available methods. We also hypothesize that different combinations of structural and functional tests will lead to the detection of glaucoma progression at an earlier point in time compared to other combinations. Finally, we hypothesize that our individualize approach will reduce the amount of time needed to detect glaucoma progression. This project will have a significant impact on the clinical management of glaucoma patients, providing clinicians with an accurate and precise method to detect glaucoma progression. This work is also highly relevant for determining clinical trial endpoints when assessing the effectiveness of new medical or surgical treatment for glaucoma.